



'''simple docstring'''






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer
def  lowerCamelCase__			(		_A							):
  # This defines a "chinese character" as anything in the CJK Unicode block:
  #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
  #
  # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
  # despite its name. The modern Korean Hangul alphabet is a different block,
  # as is Japanese Hiragana and Katakana. Those alphabets are used to write
  # space-separated words, so they are not treated specially and handled
  # like the all of the other languages.
  if (
      (cp >= 0X4_E00 and cp <= 0X9_FFF)
      or (cp >= 0X3_400 and cp <= 0X4_DBF)  #
      or (cp >= 0X20_000 and cp <= 0X2A_6DF)  #
      or (cp >= 0X2A_700 and cp <= 0X2B_73F)  #
      or (cp >= 0X2B_740 and cp <= 0X2B_81F)  #
      or (cp >= 0X2B_820 and cp <= 0X2C_EAF)  #
      or (cp >= 0XF_900 and cp <= 0XF_AFF)
      or (cp >= 0X2F_800 and cp <= 0X2F_A1F)  #
  ):  #
    return True

  return False
def  lowerCamelCase__			(		_A							):
  # word like '180' or '身高' or '神'
  for char in word:
    a		:   List[str]	  =	ord(_A							)
    if not _is_chinese_char(_A							):
      return 0
  return 1
def  lowerCamelCase__			(		_A							):
  a		:   str	  =	set()

  for token in tokens:
    a		:   Union[str, Any]	  =	len(_A							) > 1 and is_chinese(_A							)
    if chinese_word:
      word_set.add(_A							)
  a		:   Optional[Any]	  =	list(_A							)
  return word_list
def  lowerCamelCase__			(		_A					,     _A							):
  if not chinese_word_set:
    return bert_tokens
  a		:   List[Any]	  =	max([len(_A							) for w in chinese_word_set]							)

  a		:   Dict	  =	bert_tokens
  a  ,      a		:   Dict	  =	0, len(_A							)
  while start < end:
    a		:   List[Any]	  =	True
    if is_chinese(bert_word[start]							):
      a		:   str	  =	min(end - start					,     _A							)
      for i in range(_A					,     1					,     -1							):
        a		:   Dict	  =	''.join(bert_word[start : start + i]							)
        if whole_word in chinese_word_set:
          for j in range(start + 1					,     start + i							):
            a		:   str	  =	'##' + bert_word[j]
          a		:   Union[str, Any]	  =	start + i
          a		:   List[Any]	  =	False
          break
    if single_word:
      start += 1
  return bert_word
def  lowerCamelCase__			(		_A					,     _A					,     _A							):
  a		:   Tuple	  =	[]

  for i in range(0					,     len(_A							)					,     100							):
    a		:   str	  =	ltp_tokenizer.seg(lines[i : i + 100]							)[0]
    a		:   Union[str, Any]	  =	[get_chinese_word(_A							) for r in res]
    ltp_res.extend(_A							)
  assert len(_A							) == len(_A							)

  a		:   Any	  =	[]
  for i in range(0					,     len(_A							)					,     100							):
    a		:   Optional[Any]	  =	bert_tokenizer(lines[i : i + 100]					,     add_special_tokens=_A					,     truncation=_A					,     max_length=512							)
    bert_res.extend(res['input_ids']							)
  assert len(_A							) == len(_A							)

  a		:   Optional[Any]	  =	[]
  for input_ids, chinese_word in zip(_A					,     _A							):
    a		:   Any	  =	[]
    for id in input_ids:
      a		:   str	  =	bert_tokenizer._convert_id_to_token(_A							)
      input_tokens.append(_A							)
    a		:   List[str]	  =	add_sub_symbol(_A					,     _A							)
    a		:   Optional[int]	  =	[]
    # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
    for i, token in enumerate(_A							):
      if token[:2] == "##":
        a		:   str	  =	token[2:]
        # save chinese tokens' pos
        if len(_A							) == 1 and _is_chinese_char(ord(_A							)							):
          ref_id.append(_A							)
    ref_ids.append(_A							)

  assert len(_A							) == len(_A							)

  return ref_ids
def  lowerCamelCase__			(		_A							):
  # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
  # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
  with open(args.file_name					,     'r'					,     encoding='utf-8'							) as f:
    a		:   List[Any]	  =	f.readlines()
  a		:   Tuple	  =	[line.strip() for line in data if len(_A							) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  a		:   Dict	  =	LTP(args.ltp							)  # faster in GPU device
  a		:   Any	  =	BertTokenizer.from_pretrained(args.bert							)

  a		:   Dict	  =	prepare_ref(_A					,     _A					,     _A							)

  with open(args.save_path					,     'w'					,     encoding='utf-8'							) as f:
    a		:   Dict	  =	[json.dumps(_A							) + '\n' for ref in ref_ids]
    f.writelines(_A							)


if __name__ == "__main__":
     lowerCAmelCase:   Dict					=		argparse.ArgumentParser(description='prepare_chinese_ref')
     parser.add_argument(
         '--file_name',
         type=str,
         default='./resources/chinese-demo.txt',
         help='file need process, same as training data in lm',
     )
     parser.add_argument(
         '--ltp', type=str, default='./resources/ltp', help='resources for LTP tokenizer, usually a path'
     )
     parser.add_argument('--bert', type=str, default='./resources/robert', help='resources for Bert tokenizer')
     parser.add_argument('--save_path', type=str, default='./resources/ref.txt', help='path to save res')

     lowerCAmelCase:   Union[str, Any]					=		parser.parse_args()
     main(args)




'''simple docstring'''






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase:   Tuple					=		logging.get_logger(__name__)

lowerCAmelCase:   Dict					=		'▁'

lowerCAmelCase:   Dict					=		{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

lowerCAmelCase:   int					=		{
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

lowerCAmelCase:   Any					=		{
    'facebook/s2t-small-librispeech-asr': 1_0_2_4,
}

lowerCAmelCase:   Optional[int]					=		['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

lowerCAmelCase:   List[Any]					=		{'mustc': MUSTC_LANGS}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       VOCAB_FILES_NAMES
    lowercase__					=       PRETRAINED_VOCAB_FILES_MAP
    lowercase__					=       MAX_MODEL_INPUT_SIZES
    lowercase__					=       ["""input_ids""", """attention_mask"""]

    lowercase__					=       []







    def __init__( self					:    int		,     __snake_case					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Optional[Any]="<s>"		,     __snake_case					:    Optional[int]="</s>"		,     __snake_case					:    Union[str, Any]="<pad>"		,     __snake_case					:    str="<unk>"		,     __snake_case					:    Dict=False		,     __snake_case					:    int=False		,     __snake_case					:    str=None		,     __snake_case					:    Optional[int]=None		,     __snake_case					:    Optional[Dict[str, Any]] = None		,     **__snake_case					:    Union[str, Any]		,     ):
      a		:   Union[str, Any]	  =	{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=__snake_case		,     eos_token=__snake_case		,     unk_token=__snake_case		,     pad_token=__snake_case		,     do_upper_case=__snake_case		,     do_lower_case=__snake_case		,     tgt_lang=__snake_case		,     lang_codes=__snake_case		,     sp_model_kwargs=self.sp_model_kwargs		,     **__snake_case		,     )
      a		:   Tuple	  =	do_upper_case
      a		:   Optional[Any]	  =	do_lower_case

      a		:   List[str]	  =	load_json(__snake_case      )
      a		:   Dict	  =	{v: k for k, v in self.encoder.items()}
      a		:   int	  =	spm_file
      a		:   Tuple	  =	load_spm(__snake_case		,     self.sp_model_kwargs      )

      if lang_codes is not None:
        a		:   Any	  =	lang_codes
        a		:   str	  =	LANGUAGES[lang_codes]
        a		:   Tuple	  =	[F"""<lang:{lang}>""" for lang in self.langs]
        a		:   str	  =	{lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""      ) for lang in self.langs}

        a		:   Optional[Any]	  =	self.lang_tokens
        a		:   Union[str, Any]	  =	tgt_lang if tgt_lang is not None else self.langs[0]

        self.set_tgt_lang_special_tokens(self._tgt_lang      )
      else:
        a		:   List[str]	  =	{}







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      return len(self.encoder      )







    @property
    def      lowercase_			( self					:    int      ):
      return self._tgt_lang







    @tgt_lang.setter
    def      lowercase_			( self					:    int		,     __snake_case					:    Optional[int]      ):
      a		:   Union[str, Any]	  =	new_tgt_lang
      self.set_tgt_lang_special_tokens(__snake_case      )







    def      lowercase_			( self					:    str		,     __snake_case					:    str      ):
      a		:   int	  =	self.lang_code_to_id[tgt_lang]
      a		:   int	  =	[lang_code_id]







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    str      ):
      return self.sp_model.encode(__snake_case		,     out_type=__snake_case      )







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    List[Any]      ):
      return self.encoder.get(__snake_case		,     self.encoder[self.unk_token]      )







    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    int      ):
      return self.decoder.get(__snake_case		,     self.unk_token      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    List[str]      ):
      a		:   List[Any]	  =	[]
      a		:   List[Any]	  =	''
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          a		:   Union[str, Any]	  =	self.sp_model.decode(__snake_case      )
          out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
          a		:   Optional[int]	  =	[]
        else:
          current_sub_tokens.append(__snake_case      )
      a		:   Tuple	  =	self.sp_model.decode(__snake_case      )
      out_string += decoded.upper() if self.do_upper_case else decoded
      return out_string.strip()







    def      lowercase_			( self					:    int		,     __snake_case					:    List[Any]		,     __snake_case					:    List[str]=None      ):
      if token_ids_a is None:
        return self.prefix_tokens + token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    List[int]		,     __snake_case					:    Optional[List[int]] = None		,     __snake_case					:    bool = False      ):

      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__snake_case		,     token_ids_a=__snake_case		,     already_has_special_tokens=__snake_case      )

      a		:   Optional[int]	  =	[1] * len(self.prefix_tokens      )
      a		:   Any	  =	[1]
      if token_ids_a is None:
        return prefix_ones + ([0] * len(__snake_case      )) + suffix_ones
      return prefix_ones + ([0] * len(__snake_case      )) + ([0] * len(__snake_case      )) + suffix_ones







    def      lowercase_			( self					:    Union[str, Any]      ):
      a		:   Union[str, Any]	  =	self.encoder.copy()
      vocab.update(self.added_tokens_encoder      )
      return vocab







    def __getstate__( self					:    Any      ):
      a		:   List[str]	  =	self.__dict__.copy()
      a		:   Union[str, Any]	  =	None
      return state







    def __setstate__( self					:    str		,     __snake_case					:    Dict      ):
      a		:   Dict	  =	d

      # for backward compatibility
      if not hasattr(self		,     'sp_model_kwargs'      ):
        a		:   int	  =	{}

      a		:   Any	  =	load_spm(self.spm_file		,     self.sp_model_kwargs      )







    def      lowercase_			( self					:    str		,     __snake_case					:    str		,     __snake_case					:    Optional[str] = None      ):
      a		:   Union[str, Any]	  =	Path(__snake_case      )
      assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
      a		:   Any	  =	save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
      )
      a		:   List[Any]	  =	save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
      )

      save_json(self.encoder		,     __snake_case      )

      if os.path.abspath(self.spm_file      ) != os.path.abspath(__snake_case      ) and os.path.isfile(self.spm_file      ):
        copyfile(self.spm_file		,     __snake_case      )
      elif not os.path.isfile(self.spm_file      ):
        with open(__snake_case		,     'wb'      ) as fi:
          a		:   Tuple	  =	self.sp_model.serialized_model_proto()
          fi.write(__snake_case      )

      return (str(__snake_case      ), str(__snake_case      ))
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   List[Any]	  =	sentencepiece.SentencePieceProcessor(**_A							)
  spm.Load(str(_A							)							)
  return spm
def  lowerCamelCase__			(		_A							):
  with open(_A					,     'r'							) as f:
    return json.load(_A							)

def  lowerCamelCase__			(		_A					,     _A							):
  with open(_A					,     'w'							) as f:
    json.dump(_A					,     _A					,     indent=2							)